Probabilistic modeling has been a dominant approach in Machine Learning research. As the field evolves, the problems of interest become increasingly challenging and complex. Makin...
Ming-Wei Chang, Lev-Arie Ratinov, Nicholas Rizzolo...
Sparse coding of sensory data has recently attracted notable attention in research of learning useful features from the unlabeled data. Empirical studies show that mapping the data...
Citizen science projects can collect a wealth of scientific data, but that data is only helpful if it is actually used. While previous citizen science research has mostly focused ...
Sunyoung Kim, Christine Robson, Thomas Zimmerman, ...
We recall the basic idea of an algebraic approach to learning Bayesian network (BN) structures, namely to represent every BN structure by a certain (uniquely determined) vector, c...
We investigate the problem of learning optimal descriptors for a given classification task. Many hand-crafted descriptors have been proposed in the literature for measuring visua...